This work aims to improve anN-gram-based statistical machine translation
system between the Catalan and Spanish languages, trained with an aligned Spanish–
Catalan parallel corpus consisting of 1.7 million sentences taken from El Periódico newspaper. Starting from a linguistic error analysis above this baseline system,
orthographic, morphological, lexical, semantic and syntactic problems are approached
using a set of techniques. The proposed solutions include the development and application
of additional statistical techniques, text pre- and post-processing tasks, and rules
based on the use of grammatical categories, as well as lexical categorization. The
performance of the improved system is clearly increased, as is shown in both human and
automatic evaluations of the system, with a gain of about 1.1 points BLEU observed in
the Spanish-to-Catalan direction of translation, and a gain of about 0.5 points in the
reverse direction. The final system is freely available online as a linguistic resource